Hire Pre-vetted Data Engineers

What is a Data Engineer?

A Data Engineer is a specialist who designs, builds, and maintains data infrastructure that enables organizations to leverage data effectively. Data Engineers do more than write SQL—they architect data pipelines, design data warehouses and lakes, optimize data flows, ensure data quality, and build systems that make data accessible to analysts and data scientists. Whether you need someone to build data infrastructure, optimize data flows, or help your organization become data-driven, a skilled Data Engineer brings infrastructure expertise and systems thinking.

What makes Data Engineers valuable is their ability to build data systems at scale. They understand distributed systems, data modeling, and infrastructure optimization. They design systems that handle massive data volumes reliably and efficiently. This is why data-driven organizations invest in Data Engineers. When you hire through Torc, you're getting someone who builds data systems that enable business insights.

Technology Stack

Data Warehousing & Lakehouses

• Snowflake, BigQuery, Redshift

• Delta Lake, Apache Iceberg

• Data warehouse design & optimization

• Dimensional modeling & schemas

ETL/ELT & Orchestration

• Apache Airflow for workflow orchestration

• dbt for transformation

• Spark for distributed processing

• Data integration tools (Fivetran, Stitch)

Distributed Processing

• Apache Spark & PySpark

• Hadoop & HDFS

• Distributed processing patterns

• Stream processing (Kafka, Flink)

Databases & Storage

• SQL databases & optimization

• NoSQL databases

• Data lakes & object storage

• Data modeling & schema design

Data Pipeline Architecture

• Real-time streaming pipelines

• Batch processing pipelines

• Lambda & Kappa architectures

• Event-driven pipelines

Key Qualities to Look For on a Data Engineer

Infrastructure Design — They design scalable data systems. They understand capacity planning, redundancy, and how systems evolve with data growth.

Problem Solving — They troubleshoot data pipeline issues systematically. They diagnose data quality problems, performance issues, and infrastructure challenges.

Systems Thinking — They understand how data systems fit within broader organizations. They design pipelines that serve multiple downstream users with different needs.

Performance Optimization — They optimize data pipelines for speed and cost. They understand query optimization, partitioning, and caching strategies.

Data Quality Focus — They care deeply about data quality. They implement validation, monitoring, and quality assurance in pipelines.

Continuous Learning — The data engineering landscape evolves rapidly. The best engineers stay current with new tools, patterns, and best practices.

Project Types Your Data Engineers Handle

Data Warehouse Design — Designing and building data warehouses. Real scenarios: Data warehouse architecture design, schema design, ETL pipeline development.

Data Lake Implementation — Building data lakes for large-scale data storage. Real scenarios: Data lake architecture, governance setup, data ingestion.

ETL Pipeline Development — Building data extraction, transformation, and loading pipelines. Real scenarios: Daily data pipelines, real-time ingestion, data synchronization.

Stream Processing — Building real-time data pipelines. Real scenarios: Event streaming pipelines, real-time analytics, Kafka pipeline development.

Data Optimization — Optimizing data systems for performance and cost. Real scenarios: Query optimization, storage optimization, cost analysis.

Data Governance — Implementing data governance and quality frameworks. Real scenarios: Data cataloging, access control, data quality monitoring.

Infrastructure Scaling — Scaling data infrastructure for growth. Real scenarios: Migration to new platforms, scaling distributed systems, architecture evolution.

Interview questions

Question 1: "Walk me through a data pipeline you designed and built. What were the requirements, what architecture did you choose, and what challenges did you encounter?"

Why this matters: Tests end-to-end data pipeline expertise. Reveals whether they understand requirements and design appropriate solutions. Shows systems thinking.

Question 2: "Tell me about a time you had to optimize a slow data pipeline. What was slow, how did you diagnose it, and what was the result?"

Why this matters: Tests performance optimization skills and systematic debugging. Reveals whether they use methodical approach versus random optimizations. Shows practical troubleshooting.

Question 3: "Describe your experience with data quality and governance in pipelines. How have you ensured data reliability and compliance?"

Why this matters: Tests responsibility toward data consumers and governance mindset. Reveals whether they treat data quality as afterthought. Shows thinking about downstream impact.